
import jieba
from wordcloud import WordCloud
import re

from PIL import Image

import matplotlib.pyplot as plt

def read_file_gbk(filename):
    with open(filename,'r',encoding='GBK') as f:
        s = f.read()
        s = re.sub('/C', '', s)
        s = re.sub('\r|\n|\s','',s)
    return s
import jieba
import numpy as np


#打开词典文件，返回列表
def open_dict(Dict = 'hahah', path=r''):
    path = path + '%s.txt' % Dict
    dictionary = open(path, 'r', encoding='utf-8')
    dict = []
    for word in dictionary:
        word = word.strip(' ,\n')
        dict.append(word)
    return dict



def judgeodd(num):
    if (num % 2) == 0:
        return 'even'
    else:
        return 'odd'


#注意，这里你要修改path路径。
deny_word = open_dict(Dict = '否定词', path= r'')
posdict = open_dict(Dict = 'positive', path= r'')
negdict = open_dict(Dict = 'negative', path= r'')
degree_word = open_dict(Dict = '程度级别词语', path= r'')
mostdict = degree_word[degree_word.index('extreme')+1 : degree_word.index('very')]#权重4，即在情感词前乘以4
verydict = degree_word[degree_word.index('very')+1 : degree_word.index('more')]#权重3
moredict = degree_word[degree_word.index('more')+1 : degree_word.index('ish')]#权重2
ishdict = degree_word[degree_word.index('ish')+1 : degree_word.index('last')]#权重0.5



def sentiment_score_list(dataset):
    seg_sentence = dataset.split('。|！|？')
    count1 = []
    count2 = []
    for sen in seg_sentence: #循环遍历每一个评论
        segtmp = jieba.lcut(sen, cut_all=False,HMM=False)  #把句子进行分词，以列表的形式返回
        i = 0 #记录扫描到的词的位置
        a = 0 #记录情感词的位置
        poscount = 0 #积极词的第一次分值
        poscount2 = 0 #积极词反转后的分值
        poscount3 = 0 #积极词的最后分值（包括叹号的分值）
        negcount = 0
        negcount2 = 0
        negcount3 = 0
        for word in segtmp:
            poscount = 0
            neg_count = 0
            poscount2 = 0
            neg_count2 = 0
            poscount3 = 0
            neg_count3 = 0
            if word in posdict:  # 判断词语是否是情感词
                poscount += 1
                c = 0
                for w in segtmp[a:i]:  # 扫描情感词前的程度词
                    if w in mostdict:
                        poscount *= 4.0
                    elif w in verydict:
                        poscount *= 3.0
                    elif w in moredict:
                        poscount *= 2.0
                    elif w in ishdict:
                        poscount *= 0.5
                    elif w in deny_word:
                        c += 1
                if judgeodd(c) == 'odd':  # 扫描情感词前的否定词数，如果为奇数：
                    poscount *= -1.0
                    poscount2 += poscount
                    poscount = 0
                    poscount3 = poscount + poscount2 + poscount3
                    poscount2 = 0
                else: # 扫描情感词前的否定词数，如果为偶数：
                    poscount3 = poscount + poscount2 + poscount3
                    poscount = 0
                a = i + 1  # 情感词的位置变化

            elif word in negdict:  # 消极情感的分析，与上面一致
                negcount += 1
                d = 0
                for w in segtmp[a:i]:
                    if w in mostdict:
                        negcount *= 4.0
                    elif w in verydict:
                        negcount *= 3.0
                    elif w in moredict:
                        negcount *= 2.0
                    elif w in ishdict:
                        negcount *= 0.5
                    elif w in deny_word:
                        d += 1
                if judgeodd(d) == 'odd':
                    negcount *= -1.0
                    negcount2 += negcount
                    negcount = 0
                    negcount3 = negcount + negcount2 + negcount3
                    negcount2 = 0
                else:
                    negcount3 = negcount + negcount2 + negcount3
                    negcount = 0
                a = i + 1
            elif word == '！' or word == '!':  ##判断句子是否有感叹号
                for w2 in segtmp[::-1]:  # 扫描感叹号前的情感词，发现后权值+2，然后退出循环
                    if w2 in posdict or negdict:
                        poscount3 += 2
                        negcount3 += 2
                        break
            i += 1 # 扫描词位置前移


            # 以下是防止出现负数的情况
            pos_count = 0
            neg_count = 0
            if poscount3 < 0 and negcount3 > 0:
                neg_count += negcount3 - poscount3
                pos_count = 0
            elif negcount3 < 0 and poscount3 > 0:
                pos_count = poscount3 - negcount3
                neg_count = 0
            elif poscount3 < 0 and negcount3 < 0:
                neg_count = -poscount3
                pos_count = -negcount3
            else:
                pos_count = poscount3
                neg_count = negcount3
            count1.append([pos_count, neg_count])
        count2.append(count1)
        count1 = []
    return count2

def sentiment_score(senti_score_list):
    score = []
    for review in senti_score_list:
        score_array = np.array(review)
        Pos = np.sum(score_array[:, 0])
        Neg = np.sum(score_array[:, 1])
        AvgPos = np.mean(score_array[:, 0])
        AvgPos = float('%.1f'%AvgPos)
        AvgNeg = np.mean(score_array[:, 1])
        AvgNeg = float('%.1f'%AvgNeg)
        StdPos = np.std(score_array[:, 0])
        StdPos = float('%.1f'%StdPos)
        StdNeg = np.std(score_array[:, 1])
        StdNeg = float('%.1f'%StdNeg)
        score.append([Pos, Neg, AvgPos, AvgNeg, StdPos, StdNeg])
    return score


def sentiment_sen(data):
    x = sentiment_score(sentiment_score_list(data))[0][4]
    y = sentiment_score(sentiment_score_list(data))[0][5]
    return x-y

#情感分析
def calculate_motion(text):
    print("emotion analyse start")
    pos=0
    neg=0
    neutral=0
    s = read_file_gbk(text)
    sentences = re.split(r' *[\.\。][\'"\)\]]* *', s)
    sen_list=[]
    for stuff in sentences:
        sen_list.append(stuff)
    # print(sen_list.__sizeof__())  计算长度：错误用法
    print(len(sen_list))   #正确用法

    for x in sen_list:
        if len(x)>0:
            if sentiment_sen(x)>0:
                pos=pos+1
            elif sentiment_sen(x)==0:
                neutral=neutral+1
            elif sentiment_sen(x)<0:
                neg=neg+1
        else:
            print(x)
    print("positive negative and neutral sentence size is为：{}、{}、{}".format(pos,neg,neutral))

    x_data = ["positive", "negative", "neutral"]
    y_data = [pos,neg,neutral]

    bar_width = 0.3

    plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签
    plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号
    # 将X轴数据改为使用range(len(x_data), 就是0、1、2...
    plt.pie(y_data, labels=x_data, autopct='%.0f%%')

    # 设置标题
    plt.title("情感计算")
    # 为两条坐标轴设置名称

    # 显示图例
    plt.legend()
    plt.show()


calculate_motion('XX.txt')
calculate_motion('YQ.txt')


